Search Results for author: Frédo Durand

Found 15 papers, 11 papers with code

FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention

1 code implementation17 May 2023 Guangxuan Xiao, Tianwei Yin, William T. Freeman, Frédo Durand, Song Han

FastComposer proposes delayed subject conditioning in the denoising step to maintain both identity and editability in subject-driven image generation.

Denoising Diffusion Personalization Tuning Free +1

Gemino: Practical and Robust Neural Compression for Video Conferencing

no code implementations21 Sep 2022 Vibhaalakshmi Sivaraman, Pantea Karimi, Vedantha Venkatapathy, Mehrdad Khani, Sadjad Fouladi, Mohammad Alizadeh, Frédo Durand, Vivienne Sze

We design Gemino, a new neural compression system for video conferencing based on a novel high-frequency-conditional super-resolution pipeline.


Can Shadows Reveal Biometric Information?

no code implementations21 Sep 2022 Safa C. Medin, Amir Weiss, Frédo Durand, William T. Freeman, Gregory W. Wornell

We transfer what we learn from the synthetic data to the real data using domain adaptation in a completely unsupervised way.

Domain Adaptation

Differentiable Rendering of Neural SDFs through Reparameterization

no code implementations10 Jun 2022 Sai Praveen Bangaru, Michaël Gharbi, Tzu-Mao Li, Fujun Luan, Kalyan Sunkavalli, Miloš Hašan, Sai Bi, Zexiang Xu, Gilbert Bernstein, Frédo Durand

Our method leverages the distance to surface encoded in an SDF and uses quadrature on sphere tracer points to compute this warping function.

Inverse Rendering

Plug-and-Play Algorithms for Video Snapshot Compressive Imaging

1 code implementation13 Jan 2021 Xin Yuan, Yang Liu, Jinli Suo, Frédo Durand, Qionghai Dai

On the other hand, applying SCI to large-scale problems (HD or UHD videos) in our daily life is still challenging and one of the bottlenecks lies in the reconstruction algorithm.

Demosaicking Denoising

AsyncTaichi: On-the-fly Inter-kernel Optimizations for Imperative and Spatially Sparse Programming

no code implementations15 Dec 2020 Yuanming Hu, Mingkuan Xu, Ye Kuang, Frédo Durand

These domain-specific optimizations further make way for classical general-purpose optimizations that are originally challenging to directly apply to computations with sparse data structures.

When and how CNNs generalize to out-of-distribution category-viewpoint combinations

2 code implementations15 Jul 2020 Spandan Madan, Timothy Henry, Jamell Dozier, Helen Ho, Nishchal Bhandari, Tomotake Sasaki, Frédo Durand, Hanspeter Pfister, Xavier Boix

In this paper, we investigate when and how such OOD generalization may be possible by evaluating CNNs trained to classify both object category and 3D viewpoint on OOD combinations, and identifying the neural mechanisms that facilitate such OOD generalization.

Object Recognition Viewpoint Estimation

Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings

1 code implementation CVPR 2020 Amy Zhao, Guha Balakrishnan, Kathleen M. Lewis, Frédo Durand, John V. Guttag, Adrian V. Dalca

We present a probabilistic model that, given a single image of a completed painting, recurrently synthesizes steps of the painting process.

DiffTaichi: Differentiable Programming for Physical Simulation

2 code implementations ICLR 2020 Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, Frédo Durand

We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators.

Physical Simulations

Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation

1 code implementation18 Apr 2019 Ronnachai Jaroensri, Camille Biscarrat, Miika Aittala, Frédo Durand

Unfortunately, the commonly used additive white noise (AWGN) models do not accurately reproduce the noise and the degradation encountered on these inputs.

Demosaicking Denoising +1

Differentiable Monte Carlo Ray Tracing through Edge Sampling

1 code implementation SIGGRAPH 2018 Tzu-Mao Li, Miika Aittala, Frédo Durand, Jaakko Lehtinen

We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters.

Inverse Rendering

Learning-based Video Motion Magnification

2 code implementations ECCV 2018 Tae-Hyun Oh, Ronnachai Jaroensri, Changil Kim, Mohamed Elgharib, Frédo Durand, William T. Freeman, Wojciech Matusik

We show that the learned filters achieve high-quality results on real videos, with less ringing artifacts and better noise characteristics than previous methods.

Motion Magnification

Deep Bilateral Learning for Real-Time Image Enhancement

2 code implementations10 Jul 2017 Michaël Gharbi, Jiawen Chen, Jonathan T. Barron, Samuel W. Hasinoff, Frédo Durand

For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms.

Image Enhancement Image Retouching

What do different evaluation metrics tell us about saliency models?

1 code implementation12 Apr 2016 Zoya Bylinskii, Tilke Judd, Aude Oliva, Antonio Torralba, Frédo Durand

How best to evaluate a saliency model's ability to predict where humans look in images is an open research question.

Cannot find the paper you are looking for? You can Submit a new open access paper.